Q&A: Dan Hanner on 2013-14 rankings

Dan Hanner is a full-time economist and part-time college basketball writer for RealGM.com. On Friday, he revealed his 2013-14 Insider "lineup-based" rankings, which you can view in full here. For an explanation of his model, click here.

Dan agreed to engage some of my hopefully not-too-dumb questions about his rankings -- including some of the biggest surprises in his list, differences between the polls, and more -- on Friday morning.

Before we hop in ... Give us a quick summary. Better yet: How would you explain your rankings to someone who had to go to office hours every day to get through his required collegiate statistics course? (Um, I'm asking for a friend.)

Hanner: As I did last year, I project the stats for every D1 player and then add these up to get a prediction for every D1 team. The difference this season is that I acknowledge that some players have more risky projections. While we might be able to make a more tight prediction for established players or high-end recruits, for most freshmen and many transfers there is a lot of risk in how they will perform. I essentially simulate the season 10,000 times, allowing player performance to vary within normal boundaries, and present the median simulation: best case and worst case for each team.

Where would you say your model differs from other statistical models?

Former Tennessee point guard Trae Golden could have a big impact at Georgia Tech. AP Photo/Wade Payne

Hanner: Most statistical models for college basketball focus on the team (focusing on things like returning minutes and recruits added), but my model starts at the player level. The advantage of a player-level model is that not all additions have the same impact everywhere. For example, transfer Trae Golden is expected to have a huge impact on Georgia Tech because the Yellow Jackets had a big hole at PG. But if he had been added to a team like Duke, with multiple quality ball-handlers, his impact would have been much smaller. The player-level model can account for these kind of differences.

Where would you say your model differs from the typical preseason AP ballot?

Hanner: The big difference relative to preseason ballots is that voters tend to focus on the best-case scenario for teams. They look at the lineup and ask, "If everything clicks, how will this team perform?" But my projections really incorporate the importance of downside. A team like Iowa (which brings nearly everyone back from the NIT runner-up squad), simply doesn’t have the same downside and thus their median simulation is higher.

But my rankings also acknowledge the importance of upside, and thus I report the best-case scenario for each team too. For so long we’ve been focused on the idea that there is simply one ranking for teams. But that paints too narrow a picture.

Kentucky clearly has a higher upside than Michigan State. But due to all the freshmen, Kentucky also has a lower downside than Michigan State. The beauty of the simulation is that we can talk about both.

A nice example of a team with a high upside might be Tennessee. They get Jeronne Maymon back this season and with Jarnell Stokes, this looks like it might be one of the more exciting teams in the SEC. But you have Tennessee at 33rd overall.

Hanner: Exactly. If you look at my rankings, you will see that Tennessee’s best-case scenarios are worthy of a Top 25 team. If everything clicks, the Volunteers should have one of the best teams in the nation. But Tennessee has more downside risk then some of the other elite teams. First, Tennessee must improve dramatically on defense, despite a slightly undersized front line. And second, transfer Antonio Barton must relearn the point guard position after not having played it the last two years at Memphis. There are simply more scenarios in which Tennessee falls apart than, say, a team like Iowa, and that is why Tennessee is lower. But this contrast (between the best and worst-case scenario for teams) is the real reason to read the rankings.

Along those lines, Illinois State seems to have an unusually high amount of variance in their ranking. What explains that?

Hanner: Statistically, top 100 juco recruits are the highest variance players. They are often stars (think Pierre Jackson), but they are also often complete busts. Illinois State has four of these players, (Daishon Knight, Michael Middlebrooks, Bobby Hunter and Zach Lofton) joining the team this year. Notably, Knight was recently arrested and is suspended, but with so many high-variance players, almost any outcome is possible for the Redbirds.

Meanwhile, a team like SMU seems to have a very tight range.

Hanner: Exactly. SMU returns five starters from last year. While they do have some key additions like top-10 juco recruit Yanick Moreira, because of what we know about the SMU lineup, we can actually make a more precise prediction about where they will finish.

Wisconsin in the top 10-15 range looks about right to me, but I can already hear the same folks who criticize Pomeroy's system for occasionally (allegedly!) overrating the Badgers airing similar concerns this season. What's your take on Wisconsin?

Sam Dekker and the Wisconsin Badgers could be much improved on offense in 2013-14. Jamie Squire/Getty Images

Hanner: Forwards Sam Dekker and Frank Kaminsky were extremely efficient last year, and if you have seen Dekker play, you realize he has a level of athleticism that Wisconsin forwards do not always have. Ben Brust is a lights-out 3-point shooter and Josh Gasser is returning after missing all last year because of injury. The tempo-free stats for all four of those players really jump off the page. But even the other players also fit a nice role. Traevon Jackson might have struggled a bit with turnovers last year, but he provides a critical ability to drive and create. This is a dangerous offensive team.

Wisconsin will look a lot different from last year, with more crisp scoring and less bruising interior defense. But no one adapts better to his personnel than Bo Ryan.

Pittsburgh at 28th also seems extremely optimistic. They were just an 8 seed in the NCAA tournament last year and the team has lost a lot of key players to graduation, the NBA draft and transfer. Why are they so high?

Hanner: I should just print up a shirt that says, “Don’t bet against Bo Ryan or Jamie Dixon.” These guys almost always find a way to finish in the top tier of their conference. But I understand why people are skeptical of this team. Jamie Dixon doesn’t have the experienced depth like he usually does. And I am predicting a precipitous fall in efficiency. Pittsburgh had the 11th-best margin-of-victory last year, and I’m projecting them to fall to 28th this season.

The real key is that Pitt has three college-level stars returning. Talib Zanna might not have received the same hype as Steven Adams, but he was Pitt’s best forward last year. Lamar Patterson is a star. And James Robinson was a surprisingly efficient freshman point guard last year. With the typical sophomore leap in development, Robinson will keep Pitt in the ACC race longer than some people expect.

Yes, we often say the biggest improvement for players comes between their freshmen and sophomore years. Do any teams stand out in that category?

Hanner: The team that really stands out is Tulsa. Danny Manning gave over half his team’s minutes to freshmen last year and I expect Tulsa to jump from 183rd to 97th this season. They still won’t be a favorite in the reconfigured CUSA, but they will be much more competitive.

We probably should talk a little more about the bottom of the rankings. You do rank all 351 teams. Of the four new entrants into D1, who do you think has the best chance to be competitive in Year 1?

Hanner: I really like Grand Canyon. Despite the complaints from Pac-12 officials, the for-profit school is on track for a competitive season. They were the most successful of the four teams in D2 last year and they have a number of athletes above the two-star level. This includes New Mexico transfer Demetrius Walker, Colorado transfer Jeremy Adams and Texas A&M transfer Daniel Alexander.